Various global or local communication networks (the Internet, the World Wide Web, local area networks and the like) offer a user a vast amount of information. The information includes a multitude of contextual topics, such as but not limited to, news and current affairs, maps, company information, financial information and resources, traffic information, games and entertainment related information. Users use a variety of client devices (desktop, laptop, notebook, smartphone, tablets and the like) to have access to rich content (like images, audio, video, animation, and other multimedia content from such networks).
The volume of available information through various Internet resources has grown exponentially in the past couple of years. Several solutions have been developed in order to allow a typical user to find the information that the user is looking for. One example of such a solution is a search engine. Examples of the search engines include Google™ search engine, Yandex™ search engine, Yahoo! ™ search engine and the like. The user can access the search engine interface and submit a search query associated with the information that the user is desirous of locating on the Internet. In response to the search query, the search engine provides a ranked list of search results. The ranked list of search results is generated based on various ranking algorithms employed by the particular search engine that is being used by the user performing the search. The overall goal of such ranking algorithms is to present the most relevant search results at the top of the ranked list, while less relevant search results would be positioned on less prominent positions of the ranked list of search results (with the least relevant search results being located towards the bottom of the tanked list of search results).
The search engines typically provide a good search tool for a search query that the user knows apriori that she/he wants to search. In other words, if the user is interested in obtaining information about the most popular destinations in Italy (i.e. a known search topic), the user could submit a search query: “The most popular destinations in Italy?” The search engine will then present a ranked list of Internet resources that are potentially relevant to the search query. The user can then browse the ranked list of search results in order to obtain information she/he is interested in as it related to places to visit in Italy. If the user, for whatever reason, is not satisfied with the uncovered search results, the user can re-run the search, for example, with a more focused search query, such as “The most popular destinations in Italy in the summer?”, “The most popular destinations in the South of Italy?”, “The most popular destinations for a romantic getaway in Italy?”.
There is another approach that has been proposed for allowing the user to discover content and, more precisely, to allow for discovering and/or recommending content that the user may not be expressly interested in searching for. In a sense, such systems recommend content to the user without an express search request based on explicit or implicit interests of the user.
As an example, when a user is viewing media content such as videos, recommended video content may appear on the user interface before, after or during interaction with the videos. Examples of platforms proposing recommended video objects include websites and applications such as YouTube™, DailyMotion™, Netflix™ and Yandex.Video™.
Methods for media object recommendation may generally be based on past user behavior, transition history and tags. However such methods may not always be satisfactory and may not always provide the most relevant recommended media objects to a user.
U.S. Patent Publication No. 2014/0279751 A1 by Ram et al. titled “Aggregation and analysis of media content information” teaches a method and apparatus for collecting and analyzing media content metadata. The technology retrieves web documents referencing media objects from web servers. Metadata of the media objects such as global tags and category weight values are generated from the web documents. Affinity values between user identities and the media objects are generated based on online behaviors of the users interacting with the media objects. Based on the affinity values and metadata of the media objects, the technology can provide recommendations of media objects.
U.S. Pat. No. 9,098,511 B1 by Lawry et al. titled “Watch time based ranking” teaches methods, systems, and apparatus, including computer programs encoded on computer storage media, for ranking search results. One of the methods includes identifying one or more sessions for a query and associating watch times of the respective resources watched in the sessions with the query. One or more watch time signals are calculated for a first resource and the query based on the watch times associated with the query. A first search result responsive to the query is obtained, wherein the first search result identifies the first resource and has an associated score S. A new score S′ is calculated based on a least S and a watch time function, the watch time function being a function of the one or more watch time signals. The new score S′ is provided to a process for ranking search results including the first search result.
U.S. Patent Publication No. 2016/0026920 A1 by Sullivan et al. titled “Online Asset Recommendation System” teaches creating a playlist of multimedia assets based on estimated user viewing length and an estimated length of engagement during a user session. Assets are selected based on an anchor asset displayed with the playlist of multimedia assets.